Modifiable Risk Factors for Stroke, Dementia, and Late-Life Depression: A Systematic Review and DALY Weighted Risk Factors for a Composite Outcome
Bibliographic record
Abstract
Abstract Background At least 60% of stroke, 40% of dementia, and 35% of late-life depression (LLD) are attributable to modifiable risk factors, with great overlap due to a shared underlying pathophysiology. This study aims to systematically identify overlapping risk factors for these diseases and calculate their relative impact on a composite outcome. Methods A systematic literature review was performed in Pubmed, Embase, and PsycInfo, between January 2000 and September 2023. We included meta-analyses reporting effect sizes of modifiable risk factors on the incidence of stroke, dementia, and/or LLD. The most relevant meta-analyses were selected, and Disability Adjusted Life Year (DALY) weighted beta-coefficients were calculated for a composite outcome. The beta-coefficients were then normalized to assess relative impact. Results Our search yielded 182 meta-analyses meeting the inclusion criteria, of which 59 were selected to calculate DALY-weighted risk factors for a composite outcome. Identified risk factors included alcohol use (normalized beta-coefficient highest category: -20), blood pressure (87), BMI (42), fasting plasma glucose (57), total cholesterol (14), leisure time cognitive activity (-54), depressive symptoms (34), diet (27), hearing loss (35), kidney function (60), pain (25), physical activity (-34), purpose in life (-30), sleep (44), smoking (58), social engagement (32), and stress (32). Discussion This study identified overlapping modifiable risk factors and calculated the relative impact of these factors on the risk of a composite outcome of stroke, dementia, and LLD. These findings could guide preventative strategies and serve as an empirical foundation for future development of tools that can empower people to reduce their risk of these diseases. Funding US National Institutes of Health and American Heart Association.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".